Skip to main content
SpringerLink
Log in

Unrestricted principal components analysis of brain electrical activity: Issues of data dimensionality, artifact, and utility

  • Published:
Brain Topography Aims and scope Submit manuscript

Summary

Principal components analysis (PCA) was performed on the 1536 spectral and 2944 evoked potential (EP) variables generated by neurophysiologic paradigms including flash VER, click AER, and eyes open and closed spectral EEG from 202 healthy subjects aged 30 to 80. In each case data dimensionality of 1500 to 3000 was substantially reduced using PCA by magnitudes of 20 to over 200. Just 20 PCA factors accounted for 70% to 85% of the variance. Visual inspection of the topographic distribution of factor loading scores revealed complex loadings across multiple data dimensions (time-space and frequency-space). Forty-two non-artifactual factors were successful in classifying age, gender, and a separate group of 60 demented patients by linear discriminant analysis. Discrimination of age and gender primarily involved EP derived factors, whereas dementia primarily involved EEG derived factors. Thirty-eight artifactual factors were identified which, alone, could not discriminate age but were relatively successful in discriminating gender and dementia. The need to parsimoniously develop real neurophysiologic measures and to objectively exclude artifact are discussed. Unrestricted PCA is suggested as a step in this direction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abt, K. Descriptive data analyses: A concept between confirmatory and exploratory data analysis. Methods of Information in Medicine, 1987, 26: 77–88.

    Google Scholar 

  • Abt, K.. Descriptive data analysis (DDA) in Quantitative EEG studies. In D. Samson-Dollfus,J. D. Guieu,J. Gotman, and P. Etvènon (Eds.), Statistics and Topography in Quantitative EEG (pp. 150–160). Amsterdam: Elsevier, 1988.

    Google Scholar 

  • Achim, A., Richer, F., Alain, C., and Saint-Hilaire, J. M. A test of model adequacy applied to the dimensionality of multichannel average auditory evoked potentials. In D. Samson-Dollfus,J. D. Guieu,J. Gotman, and P. Etvenon (Eds.), Statistics and Topography in Quantitative EEG (pp. 161–171). Amsterdam: Elsevier, 1988.

    Google Scholar 

  • Bartels, P. H. Numerical evaluation of cytologic data IX. Search for data structure by principal components transformation. Anal. Quant. Cytol., 1981a, 3: 167–177.

    Google Scholar 

  • Bartels, P. H. Numerical evaluation of cytologic data VIII. Computation of the principal components. Anal. Quant. Cytol., 1981b, 3: 83–90.

    Google Scholar 

  • Bartlett, M. S. Tests of significance in factor analysis. Brit. J. of Stat. Psychol., 1950, 3: 77–85.

    Google Scholar 

  • Bickford, R. G., Cody, D. T., Jacobsen, J. L., and Lambert, E. H. Fast motor systems in man: Physiopathology of the sonomotor response. Trans. Am. Neurol. Assoc., 1964, 89: 56–58.

    Google Scholar 

  • Cooley, W. W., and Lohnes, P. R. Multivariate Data Analysis. New York: J. Wiley and Sons, 1971.

    Google Scholar 

  • Donchin, E. A multivariate approach to the analysis of average evoked potentials. IEEE Trans. Biomed. Eng., 1966, BME-13: 131–139.

    Google Scholar 

  • Duffy, F. H. Topographic display of evoked potentials: Clinical applications of brain electrical activity mapping (BEAM). Ann. N.Y. Acad. Sci., 1982, 388: 183–196.

    Google Scholar 

  • Duffy, F. H. Clinical decision making in quantified electroencephalographic analysis. In D. Samson-Dollfus,J. Gotman,J. D. Guieu, and P. Etevenon (Eds.), Statistics and Topography in Quantitative EEG (pp. 9–26). Paris: Elsevier, 1988.

    Google Scholar 

  • Duffy, F. H.. Topographic mapping of brain electrical activity: Clinical applications and issues. In K. Maurer (Eds.), Topographic Brain Mapping of EEG and Evoked Potentials (pp. 19–52). Heidelberg, Germany: Springer-Verlag, 1989.

    Google Scholar 

  • Duffy, F. H., Albert, M. S., and McAnulty, G. Brain electrical activity in patients with presenile and senile dementia of the Alzheimer's type. Ann. Neurol., 1984a, 16: 439–448.

    Google Scholar 

  • Duffy, F. H., Albert, M. S., McAnulty, G., and Garvey, A. J. Age- related differences in brain electrical activity mapping of healthy subjects. Ann. Neurol., 1984b, 16: 430–438.

    Google Scholar 

  • Duffy, F. H., Bartels, P. H., and Neff, R. N. A response to Oken and Chiappa. Ann. Neurol., 1986, 19: 494–495.

    Google Scholar 

  • Duffy, F. H., Burchfiel, J. L., and Lombroso, C. T. Brain electrical activity mapping (BEAM): A method for extending the clinical utility of EEG and evoked potential data. Ann. Neurol., 1979, 5: 309–321.

    Google Scholar 

  • Duffy, F. H., Jones, K., Bartels, P., Albert, M., McAnulty, G. B., and Als, H. Quantified neurophysiology with mapping: Statistical inference, exploratory and confirmatory data analysis. Brain Topog., 1990, 3: 3–12.

    Google Scholar 

  • Gasser, T., Bacher, P., and Mocks, J. Transformation towards the normal distribution of broad band spectral parameters of the EEG. Electroencephalogr. Clin. Neurophysiol., 1982,. 53: 119–124.

    Google Scholar 

  • Golub, G. H. Matrix Computations (2 ed.). Baltimore, MD: Johns Hopkins University Press, 1989.

    Google Scholar 

  • Hachinski, J. Cerebral blood differentiation of Alzheimer's disease from multi-infarct dementia. In R. Katzman,R. D. Terry, and K. T. Bick (Eds.), Alzheimer's Disease: Senile Dementia and Related Disorders (pp. 97–104). New York: Raven Press, 1978.

    Google Scholar 

  • Harner, R. N., and Riggio, S. Application of singular value decomposition to topographic analysis of flash-evoked potentials. Brain Topog., 1989, 2: 91–98.

    Google Scholar 

  • Harner, R. N., Warner, J. A., and Riggio, S. Localization of disordered brain function by singular value decomposition and spatial rotation of orthogonal features of the multichannel flash-evoked potential(in press). Brain Topog., 1991.

  • Horst, P. Factor Analysis of Data Matrices. New York: Holt, Rinehart, Winston, 1965. Hotelling, H. Analysis of complex statistical variables into principal components. J. Ed. Psychol., 1933, 24: 417–441.

    Google Scholar 

  • John, E. R., Karmel, B. Z., Corning, W. C., Easton, P., Brown, D., Ahn, H., John, M., Harmony, T., Prichep, L., Toro, A., Gerson, I., Bartlett, F., Thatcher, R., Kaye, H., Valdes, P., and Schwartz, E. Neurometrics. Science, 1977, 196: 1393–1410.

    Google Scholar 

  • John, E. R., Prichep, L. S., Fridman, J., and Easton, P. Neurometrics: Computer-assisted differential diagnosis of brain dysfunctions. Science, 1988, 239: 162–169.

    Google Scholar 

  • John, E. R., Walker, P., Cawood, D., Rush, M., and Gehrmann, J. Factor analysis of evoked potentials. Electroencephalogr. Clin. Neurophysiol., 1973, 24: 33–43.

    Google Scholar 

  • Jones, K. J. Relation of R-analysis factor scores and Q-analysis factor loadings. Psychol. Reports, 1967, 20: 247–249.

    Google Scholar 

  • Kaiser, H. F. The application of electronic computers to factor analysis. Ed. Psych. Measmt., 1960, 20: 141–151.

    Google Scholar 

  • Kaiser, H. J. A Varimax criterion for analytic rotation in factor Analysis. Psychometrika, 1958,. 23: 187–200.

    Google Scholar 

  • Kaiser, H. J. A computer program for Varimax rotation factor analysis. Ed. Psych. Measmt., 1959, 19: 413–420.

    Google Scholar 

  • Kavanagh, R. N., Darcey, T. M., and Fender, D. H. The dimensionality of the human visual evoked scalp potential. Electroencephalogr. Clin. Neurophysiol., 1976, 40: 633–644.

    Google Scholar 

  • Lopes da Silva, F. H.. Event-related potentials: Methodology and quantification. In E. Niedermeyer and F. Lopes da Silva (Eds.), Electroencephalography Basic Principles, Clinical Applications and Related Fields (pp. 763–772). Baltimore-Munich: Urban and Schwarzenberg, 1987.

    Google Scholar 

  • Maier, J., Dagnelie, G., Spekreijse, H., and van Dijk, B. W. Principal components analysis for source localization of VEPs in man. Vision Res., 1987, 27: 165–177.

    Google Scholar 

  • Molfese, D. L., Buhrke, R. A., and Wang, S. L. The right hemisphere and temporal processing of consonant transition durations: electrophysiological correlates. Brain Lang., 1985,. 26, 49–62.

    Google Scholar 

  • Oken, B. S., and Chiappa, K. H. Statistical issues concerning computerized analysis of brainwave topography. Ann. Neurol., 1986, 19: 493–494.

    Google Scholar 

  • Rawlings, R. R., Rohrbaugh, J. W., Begleiter, H., and Eckardt, M. J. Spectral methods for principal components analysis of event-related brain potentials. Comput. and Biomed. Res., 1968, 19: 497–507.

    Google Scholar 

  • Rogers, L. J., and Douglas, R. R. Comparative factor analysis models for an empirical study of EEG data, III: resolution of stationary generating signals with confirmation by variable deletion. Internat. J. Neurosci., 1984, 24: 167–183.

    Google Scholar 

  • Rösler, F., and Manzey, D. Principal components and varimax-rotated components in event-related potential research: some remarks on their interpretation. Biol. Psychiatry, 1981, 13: 3–26.

    Google Scholar 

  • Scherg, M., and Von Cramon, D. Two bilateral sources of the late AEP as identified by a spatio-temporal dipole model. Electroencephalogr. Clin. Neurophysiol., 1985, 62: 32–44.

    Google Scholar 

  • Seal, H. L.. Principal components. In H. L. Seal (Eds.), Multivariate Statistical Analysis for Biologists (pp. 101–122). New York: John Wiley and Sons, 1964.

    Google Scholar 

  • Semlitsch, H. V., Anderer, P., Schuster, P., and Presslich, O. A solution for reliable and valid reduction of ocular artifacts, applied to the P300 ERP. Pschophysiol., 1986, 23: 695–703.

    Google Scholar 

  • Silberstein, R. B., Cadusch, P. J., and Schier, M. A. Effects of conduction on spatial components analysis applied to brain electrical activity. Brain Topography, 1992, 4(4): 267–276.

    Google Scholar 

  • Skalka, H., and Holman, J. Effect of pupillary dilatation in flash VER testing. Doc. Ophthalmol., 1986, 63: 321–324.

    Google Scholar 

  • Skrandies, W., and Lehmann, D. Spatial principal components of multichannel maps evoked by lateral visual half-field stimuli. Electroencephalogr. Clin. Neurophysiol., 1982, 54: 662–667.

    Google Scholar 

  • Suter, C. M. Principal component analysis of average evoked potentials. Exp. Neurol., 1970, 29(2): 317–327.

    Google Scholar 

  • Valdes, P., Bosch, J., Grave, R., Hernandez, J., Riera, J., Pascual, R., and Boscay, R. Frequency domain models of the EEG. Brain Topography, 1992, 4(4): 309–319.

    Google Scholar 

  • Van Rotterdam, A. Limitations and difficulties in signal processing by means of the principal-components analysis. I.E.E.E. Trans. Biomed. Eng., 1970,. BME-17: 268–269.

    Google Scholar 

  • Wastell, D. G. On the independence of P300 and the CNV: A short critique of the principal components analysis of donchin et al. (1975). Biol. Psychiat., 1979, 9: 171–176.

    Google Scholar 

  • Weintraub, S., Baratz, R., and Mesulam, M. M. Assessment of daily living in Alzheimer's disease. In S. Corkin,K. L. Davis, and J. H. Growdon (Eds.), Alzheimer's Disease: A Review of Progress (pp. 189–192). New York: Raven Press, 1982.

    Google Scholar 

  • Wood, C. C., and McCarthy, G. Principal component analysis of event-related potentials: Simulation studies demonstrate misallocation of variance across components. Electroenceph. Clin. Neurophysiol., 1984, 59: 249–260.

    Google Scholar 

  • Zar, J. H. Biostatistical Analysis. Englewood Cliffs, N. J.: Prentice Hall, 1984.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Neurology, Childrens Hospital and Harvard Medical School, 300 Longwood Avenue, 02115, Boston, MA, USA

    Frank H. Duffy & Gloria McAnulty

  2. Florence Heller Graduate School for Advanced Studies in Social Welfare, Brandeis University, Waltham, MA, USA

    Kenneth Jones

  3. Optical Sciences Center, University of Arizona, Tuscon, AZ, USA

    Peter Bartels

  4. Departments of Neurology and Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Marilyn Albert

Authors
  1. Frank H. Duffy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kenneth Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter Bartels
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gloria McAnulty
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marilyn Albert
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Acknowledgements: This work was supported in part by NIA program project PO1AG049853 to M. Albert and the Mental Retardation Program Project P30HD18655 to J.J. Volpe. We thank our qEEG technologists Adele Mirabella, Susan Katz, Ellen Belles, and Marianne McGaffigan as well as our research secretaries for their unflagging support.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Duffy, F.H., Jones, K., Bartels, P. et al. Unrestricted principal components analysis of brain electrical activity: Issues of data dimensionality, artifact, and utility. Brain Topogr 4, 291–307 (1992). https://doi.org/10.1007/BF01135567

Download citation

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01135567

Key words

Search

Navigation